'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
